Copyright © by the paper’s authors. Copying permitted for private and academic purposes.
In: R. Orji, M. Reisinger, M. Busch, A. Dijkstra, M. Kaptein, E. Mattheiss (eds.): Proceedings of the Personalization in Persuasive Technology Workshop, Persuasive Technology 2017, Am- sterdam, The Netherlands, 04-04-2017, published at http://ceur-ws.org
Persuasive Technology and the Effect of Age and Gender
Kiemute Oyibo1, Rita Orji2, and Julita Vassileva1
1 University of Saskatchewan, Saskatoon, Canada {kiemute.oyibo@usask.ca,jiv@cs.usask.ca}
2 University of Waterloo, Waterloo, Canada {rita.orji@uwaterloo.ca}
Abstract. Research has shown that social influence is a strong motivator of behavior change. However, in persuasive technology research, limited studies exist showing the influence of age and gender on its effectiveness. To bridge this gap, we conducted a study among 323 participants on the level of susceptibility to four social influence strategies: Social Learning, Social Comparison, Compe- tition and Reward. Our results reveal that 1) males and females vary in their level of susceptibility to Reward and Competition, with males being more susceptible than females; and 2) younger and older individuals vary also, with younger indi- viduals being more susceptible to Competition, Social Comparison and Social Learning. Specifically, our results reveal that Competition, a powerful driver of intrinsic motivation, is most effective in bringing about behavior change in younger males, but least effective in older females. These findings provide de- signers with insight into effective ways of tailoring persuasive applications (using commonly applied gamification mechanics) based on age and gender.
Keywords: persuasive strategies, gamification, social influence, social compar- ison, social learning, reward, competition, intrinsic motivation.
1 Introduction
In recent years, persuasive applications, aimed at changing attitudes and behaviors, have become widespread, cutting across different domains, such as commerce and health [1]. However, most of the persuasive applications in the marketplace have been designed mainly based on general user requirements and designers’ judgments and ex- periences [2]. In most cases, established behavior change theories and/or empirical ev- idence have not been used to inform their design, thereby making it difficult to evaluate persuasive technologies as to which persuasive strategy works or does not [3]. Whereas, research has shown that applications developed based on user models are likely to be more effective than those that are not [4]. Moreover, social influence has been found to be a powerful means to change human behaviors [5]. According to Cialdini and Trost [6], social influence “can be employed to foster growth and move people away from
negative habits and in more positive directions, thereby creating the conditions for new change opportunities” (p. 151).
However, in persuasive technology research, limited empirical evidence exists showing which social influence strategies users are likely to be more susceptible to and how demographic variables, such as age and gender, moderate their susceptibility. In this paper, as part of our ongoing research [7], aimed at investigating possible effective social influence strategies to motivate people to change their behavior in the physical activity domain, we carried out a study among 323 participants to uncover the most effective persuasive strategies participants are most susceptible to and the role gender and age play. We used Busch et al.’s [8] Persuadability Inventory, which include Re- ward, Competition, Social Learning and Social Comparison, to uncover how suscepti- ble individuals are to social influence.
The results of our analysis reveal that all four social influence strategies are poten- tially effective in bringing about behavioral change using persuasive technology, as participants, irrespective of gender and age, rated them as persuasive, i.e., above the neutral score of 3.5. Overall, Reward is the most persuasive strategy, followed by Com- petition, while Social Learning and Social Comparison are the least persuasive. More- over, with respect to gender, our results reveal that males are more motivated by Re- ward and Competition than females. Similarly, with respect to age, the results show that younger people are more motivated by Competition, Social Learning and Social Comparison than older people. More importantly, younger males are more motivated by Competition, while older females are least motivated by Competition. This suggests that while Competition may be a very effective strategy for younger males, it may be less effective for older females. This underscores the need for personalization of per- suasive technologies based on age and gender. Consequently, these findings will help designers of persuasive technologies to design more effective persuasive applications by tailoring based on age and gender.
2 Background
In this section, we provide a brief overview of the four social influence strategies for which Busch et al. [8] developed measurement instruments, called Persuadability In- ventory (PI). We also provide an overview of the socio-psychology theories on which these four strategies are based.
Reward. Reward is a persuasive strategy, which derives from the Incentive Theory of Motivation [9]. According to the theory, human behavior is primarily motivated by extrinsic factors, such as incentive, praise or reward [9, 10]. Reward is something which is offered to “an individual as a result of the accomplishment of a specific task or the achievement of a target behavior” [11]. In persuasive technology research, Reward is modeled as a construct for measuring how well Reward as a persuasive strategy can persuade people to perform a target behavior. In empirical research, it is operationalized as a set of questions. A typical question from the PI [8] is “I put more ambition into
something if I know I am going to be rewarded for it.” In persuasive applications, Re- ward can be implemented as virtual points, badges, etc. [11].
Competition. Competition is a persuasive strategy, which derives from the Theory of Competition [12]. According to Mead [13], Competition is “the act of seeking or endeavoring to gain what another is endeavoring to gain at the same time” (p. 8). Un- like rivalry, it is oriented towards a primary goal, with other competitors for the goal being secondary [14]. In persuasive technology research, Competition is modeled as a construct for measuring how well it can be used as a persuasive strategy to motivate users intrinsically to perform the target behavior. In empirical research, it is operation- alized as a set of questions. An example question from the PI [8] is “It is important to me to be better than other people” [11]. In persuasive applications, it can be imple- mented as a leaderboard on which users’ performance are displayed [15] . The leader- board allows users to know their relative position in the performance of the target be- havior. Basically, it allows users to view and compare their performance of a target behavior with the performance of other users of the persuasive application [16].
Social Comparison. Social Comparison is a persuasive strategy, which has been adapted from the Social Comparison Theory postulated by Festinger [17]. The theory states that people compare their opinions and abilities with the opinions and abilities of others, with the intention of improving themselves [18]. According to Festinger [17], if people intend to improve themselves, they compare themselves with people who are superior to them. This type of interpersonal comparison is known as upward compari- son. On the other hand, if they intend to enhance their self-esteem, they compare them- selves with people who are inferior. This type of comparison is known as downward comparison. In persuasive technology empirical research, Social Comparison is mod- eled as a construct for measuring how well Social Comparison as a persuasive strategy can persuade people to perform a target behavior [11]. In empirical studies, it is opera- tionalized as a set of questions, one of which, from the PI [8], is “I like to compare myself with other people.” Further, Social Comparison can be implemented in persua- sive applications by providing users with the ability to view and compare their perfor- mance with that of others [19, 20], e.g., step count, distance walked or cycled, calories burnt, time spent exercising, etc.
Social Learning. Social Learning is a persuasive strategy, which is derived from the Social Learning Theory developed by Bandura [21]. The theory holds that the learning of an individual is a social cognitive process, which involves observing the behaviors of other people and their consequences. In persuasive technology research, Social Learning, also known as Social Proof or Consensus [12], is modeled as a construct for measuring how well Social Learning as a persuasive strategy can persuade people to engage in a target behavior [11]. It is operationalized as a set of questions in empirical research. A typical question from the PI [8] is “I take other people as role models for new behaviors.” In persuasive applications, it is implemented in a number of ways, e.g., informing users about the behaviors of other users of the application with the intention
of persuading them to act in a similar way. For example, the persuasive message “Peo- ple who bought item A also bought item B” (as seen in Amazon’s website) is intended to persuade online shoppers to purchase item B after purchasing item A [22].
3 Related Work
Though social influence is considered a powerful means to motivate beneficial behav- iors in persuasive technology [5], limited research has been carried out on the respon- siveness of individuals to social influence strategies. Busch et al. [8] carried out a study to develop measurement instruments (which they called Persuadability Inventory) for five social influence strategies, which they adapted from the Persuasive System Design (PSD) model proposed by Kaptein et al. [23]. Furthermore, Oyibo and Julita [11] car- ried out a study in which they investigated how four of the inventory’s instruments (Reward, Competition, Social Learning and Social Comparison) influence one another in a path model analysis, with Competition as the target (predicted) construct. They found that the susceptibility of individuals to Reward and Social Comparison are good predictors of their persuadability by Competition. However, both groups of authors did not investigate the level of susceptibility of individuals to each of the five persuasive strategies. Moreover, in validating the instruments in Busch et al.’s [8] original study, over 95% of the items did not meet the internal consistency reliability requirement (i.e., Cronbach alpha >= 0.7). One possible reason for the poor reliability is the limited sam- ple size (n = 167). Consequently, in our study, in addition to our main objective of determining the social influence strategies individuals are more susceptible to and the role gender and age play, we set out to validate the Persuadability Inventory by testing the internal consistency reliability of its scales using a larger sample size (n = 323), which approximately doubles Busch et al.’s [8] sample size (n = 167).
4 Method
In this section, we present our research hypotheses, the instruments used to measure the social influence constructs of interest and the demographics of participants.
4.1 Research Hypotheses
Research has shown that social influence can be leveraged as a persuasive strategy to effect behavior change that benefits both the individual and society. In this paper, we attempt to address the research questions: 1) “Which of the social influence strategies in Busch et al.'s [8] Persuadability Inventory (PI) are individuals more susceptible to?”
2) “How do gender and age affect the effectiveness of these strategies?” Based on the PI (adapted from the PSD model [12]) and existing theories and findings in the litera- ture, we formulated the following eight hypotheses:
H1: Males are more persuadable by Competition than females.
H2: Males are more persuadable by Reward than females.
H3: Females are more persuadable by Social Comparison than males.
H4: Females are more persuadable by Social Learning than males.
H5: Younger people are more persuadable by Competition than older people.
H6: Younger people are more persuadable by Reward than older people.
H7: Younger people are more persuadable by Social Comparison than older people.
H8: Younger people are more persuadable by Social Learning than older people.
The first hypothesis (H1: males are more persuadable by Competition than females) was informed by the work of Niederle and Lise [24] on gender difference in competi- tiveness. They found that males were more competitive than females. Thus, we hypoth- esize that males will be more susceptible to Competition as a social influence strategy than females. The second hypothesis (H2: males are more persuadable by Reward than females) is informed by the work of Li et al. [25], which was based on the Sensitivity to Punishment and Sensitivity to Reward Questionnaire (SPSRQ). They found that males were more sensitive to Reward than females. Thus, we hypothesize that males will be more responsive to Reward as a persuasive strategy than females. The third hypothesis (H3: females are more persuadable by Social Comparison than males) is informed by the notion that males focus on their personal uniqueness and hardly define themselves in the context of relationships [26]. Thus, they are viewed by society as more independent. However, females tend to define themselves in the context of their interpersonal relationships. Thus, they are viewed by society as more interdependent [26]. Moreover, in a study on body comparison tendencies, Franzoi et al. [27] found that women were more likely to compare their face and bodies to similar others’ than men. Based on these findings, we hypothesize that females will be more susceptible to Social Comparison than males. Furthermore, the fourth hypothesis (H4: females are more persuadable by Social Learning than males) is informed by Orji et al.’s [28] work on Cialdini’s principles of persuasion. Specifically, they found that females are more persuadable by Consensus (i.e., Social Learning) than males.
With respect to age, the fifth hypothesis (H5: younger people are more persuadable by Competition than older people) is based on Yee’s [29] study on the effect of age on competition. He found that the appeal of competition to gamers drops with age. Conse- quently, we hypothesize that younger adult will be more persuadable by Competition than younger adults. The sixth hypothesis (H6: younger people are more persuadable by Reward than older people) is based on Sproten and Schwieren [30] study on age difference in the reaction to incentives. They found that social incentives motivate men to improve their performance more than women. Based on this finding, we hypothesize that younger adults will be more persuadable by Reward than older adults. The seventh hypothesis (H7: younger people are more persuadable by Social Comparison than older people) is based on Callan et al.’s [3] study on the individual tendency to engage in social comparison. They found that older adults are less likely to engage in social comparison than younger adults. Based on this finding, we hypothesize that younger adults will be more persuadable by Social comparison than older adults. Finally, the eight hypothesis (H8: younger people are more persuadable by Social Learning than older people) is based on prior research findings on the influence of peer pressure on adolescents. As cited by Steinberg and Monahan [31], adolescents tend to alter their behaviors because they want to fit in: they care more about what their friends think of them and, as a result, prefer to go along with the crowd in order to avoid being rejected.
Based on this finding, we hypothesize that younger individuals will be more persuada- ble by Social Learning than older individuals.
4.2 Measurement Instruments
We used four of the five validated scales in Busch et al.’s [8] PI to measure the con- structs of interest: Reward (6 items), Social Comparison (6 items), Social Learning (5) and Competition (5 items). We dropped the fifth construct in the PI (Trustworthiness) because it has limited (three) validated items, which might likely affect its content va- lidity and reliability. Each of the four constructs comprises a nine-point Likert scale, ranging from “Completely Disagree (1)” to “Completely Agree (9)”. In order to prevent participants from knowing which specific construct was being measured at any given time in the online survey, we selected approximately equal number of items from all four scales, combined and randomized them in each webpage of the survey.
4.3 Participants
Our study was approved by the University of Saskatchewan Research Ethics Board.
Respondents were invited to participate in the online survey via email, the university’s website, Facebook and Amazon Mechanical Turk (AMT). The AMT participants were compensated with $0.8 each, while the others were given a chance to win a C$50 gift card. A total of 323 subjects completed the survey. Among them, 42.1% were males, 56.0% were females. Age-wise, 39.9% were between 18 and 24, 42.7% were between 25 and 34, and 17.3% were above 34. Education-wise, 28.2% had high school educa- tion, 40.6% had bachelor degrees and 19.8% had postgraduate degrees. Lastly, 61.0%
were from North America, 21.4% were from Africa and 9.3% were from Asia.
Table 1. Demographics of participants N = 323
Gender Male (42.1%) Female(56.0%); Unknown (1.9%) Age 18-24 (39.9%); 25-34 (42.7%); > 34 (17.3%)
Education Technical/Trade School (8.7%); High School (28.2%); Bachelor Degree (40.6%); Postgraduate Degree (19.8%); Others (2.7%)
Continent North America (61.0%); Africa (21.4%); Asia (9.3%); Others (8.4%)
5 Results
In this section, we present the construct reliability test, the aggregated means of each construct and our between-group and within-group parametric analyses.
5.1 Reliability of Measures
Using the scaleReliability function in the userfriendlyscience package in R, we carried out the internal consistency reliability test for the four social influence constructs based on McDonald’s omega (ω) and Cronbach’s alpha (α) coefficients. However, given that our data did not meet the normal-distribution criterion, the former metric is considered
appropriate as a measure of the reliability of the constructs [32]. As shown in Table 2, based on both metrics, our data met the reliability requirement of ω >= 0.7 [33].
Table 2. Internal consistency reliability for the social influence constructs McDonald’s Coefficient Cronbach’s Coefficient Construct Omega (ω) Confidence Interval Alpha (α) Confidence Interval
Competition 0.76 [0.71, 0.80] 0.75 [0.71, 0.79]
Reward 0.82 [0.79, 0.85] 0.82 [0.72, 0.85]
Social Comparison 0.79 [0.75, 0.82] 0.78 [0.75, 0.82]
Social Learning 0.83 [0.80, 0.86] 0.82 [0.79, 0.85]
5.2 Mean Ratings of Strategies
Fig. 1 shows the overall (aggregated) mean score of each strategy, with error bars rep- resent 95% confidence interval. Overall, all of the four social influence strategies were perceived as persuasive as the respective scores are above the neutral score of 3.5.
Moreover, participants perceived Reward as the most persuasive socially influential strategy, followed by Competition. However, Social Learning and Social Comparison were perceived as least persuasive. Our non-parametric pairwise comparisons using Nemenyi post-hoc test shows that each pair of the strategies significantly differs at p <
0.0001, except for the Social Comparison/Social Learning pair, which p-value = 0.06.
Fig. 1. Overall aggregated mean scores of social influence strategies
5.3 Between Group Analysis and Interaction Effect
We carried out between-group analysis based on gender and age. Specifically, based on age, we split the data into two categories: younger participants (18-24 years old) and older participants (above 24 years old). We used the age 24 for the split because it resulted in the best almost-equal halves, with the younger and older groups comprising approximately 40% and 60% of the participants respectively. Fig. 2 shows the plot of the mean scores based on gender and age. Based on gender, we found some differences between males and females with respect to Competition and Reward. Similarly, based on age, we found some differences between the two groups with respect to Competition, Social Comparison and Social Learning. To investigate whether these differences were statistically significant, we carried out non-parametric between-group analysis (Krus- kal-Wallis rank sum test) as shown in Table 2. The result reveals that 1) based on gen- der, males perceived Competition and Reward more persuasive than females; and 2) based on age, younger participants perceived Competition, Social Comparison and So- cial Learning more persuasive than older participants. Lastly, we carried out an inter- action effect analysis. The result shows no interaction effect between age and gender.
Fig. 2. Aggregated mean scores of social influence strategies based on gender and age
Table 2. Mean scores of social influence strategies and group difference significance test (the bold values indicate significant difference between two groups)
Construct Overall Male Female Sig. Young Old Sig.
Competition 5.94 6.43 5.59 0.001 6.32 5.69 0.01
Reward 6.71 7.03 6.44 0.001 6.79 6.65 n.s
Social Compare 5.34 5.39 5.27 n.s 5.69 5.11 0.01
Social Learning 5.52 5.56 5.44 n.s 5.86 5.29 0.001
Table 3. Summary of supported and unsupported hypotheses
No. Description of Hypothesis Supported
H1 Males are more persuadable by Competition than females H2 Males are more persuadable by Reward than females H3 Females are more persuadable by Social Comparison than males. Χ H4 Females are more persuadable by Social Learning than males. Χ H5 Younger people are more persuadable by Competition than older peo-
ple.
H6 Younger people are more persuadable by Reward than older people. Χ H7 Younger people are more persuadable by Social Comparison than older
people.
H8 Younger people are more persuadable by Social Learning than older
people.
6 Discussion
The paper presents the results from investigating the gender and age differences in the persuasiveness of four social influence strategies (Competition, Reward, Social Com- parison, and Social Learning) that have been widely employed in the design of persua- sive technological interventions. In addition, we investigated the overall and the com- parative persuasiveness of the strategies. As a secondary objective, we investigated the internal consistency reliability of the operationalized constructs. Our reliability test re- veals that all of the four social influence constructs we investigated are reliable, thereby providing evidence of stronger reliability than Busch et al. [8] was able to achieve in their original study on the development of the measurement instruments for the con- structs, perhaps, due to a relatively small sample size.
6.1 Overall and Comparative Persuasiveness
Our results show that participants perceive all the four strategies as persuasive as the average rating of each strategy is higher than the neutral rating of 3.5. Comparatively, Reward emerged as the most persuasive overall, followed by competition. This implies that in a one-size-fits-all design approach, designers should choose the Reward and Competition strategies over the Social Comparison and Social Learning to increase the efficacy of their persuasive systems. The results also suggest that although Reward has been a controversial strategy because of its tendency to redirect the intention of behav- ior from intrinsic to extrinsic [20], it appeals to many people. This is probably because Reward has the tendency to provide an immediate reinforcement and present users something to work for since it is often difficult to visualize the short-term benefit of most behavior. These findings are in line with existing literature, Reward and Compe- tition are among the most frequently and widely employed strategies in persuasive and gamified system design [34].
6.2 Tailoring Persuasive Technology Based on Gender
Our results show that males are more responsive to the Reward and Competition strat- egies than females. This result is in line with Orji et al. [35] and Busch et al. [36] who found that males are more responsive to the Competition strategy than females in the context of healthy eating and physical activity respectively. One explanation for this finding is that males who are often more confident and domineering than females are more inclined to the competition because it provides an opportunity for them to over- power or outperform others and show their superiority. Males are even more motivated when they are rewarded as a way of confirming their superiority and ingenuity than females. The findings imply that males can be more easily persuaded using Reward and Competition strategies. Thus, applications tailored for males should employ the Reward and Competition strategy (above the comparison and social learning) to motivate be- havior change. On the other hand, females are more motivated by Social comparison and Social Learning (although the difference is not significant). Again, this is not sur- prising considering that females tend to be more interdependent and influenced by group opinions than males. This finding contradicts that of Busch et al. [36], who found that males are more responsive to the comparison strategy than females in the context of persuasive technology for motivating physical activity. One possible explanation for this difference is that individual’s susceptibility to a persuasive strategy may be context- dependent [4]. A persuasive strategy that works well in one domain may fail in another domain. Busch et al. [36] focused on physical activity while the current study investi- gated persuasiveness in a more general context. The results suggest that the persuasive- ness of some strategies such as Social Comparison may be domain-dependent; hence, more research is needed to establish this.
6.3 Tailoring Persuasive Technology Based on Age
Our findings show that younger individuals are more responsive to all of the four social influence strategies (Competition, Reward, Social Comparison, and Social Learning) than older individuals, though there was no statistically significant difference between both groups with respect to Reward. This is in line with Orji et al. [28] who found that younger adults are more persuadable than older adults. This suggests that younger peo- ple are more likely to evaluate persuasive appeals via the peripheral route (without thoughtfully considering the arguments) compared to the older individuals. The impli- cation is that an emotional persuasive approach may work better for younger adults than older adults [28]. Most social influence persuasive strategies motivate by appeal- ing to an individual’s emotional thinking rather than logical thinking; therefore, they are more appropriate for motivating younger people. Hence, further research is needed to identify older-adult-oriented strategies that can persuade via the central route and appeal to an individual’s sense of reasoning.
7 Conclusion
This paper presents the results of an empirical study among 323 participants, which investigated gender and age differences in the persuasiveness of four social influence strategies: Competition, Reward, Social Comparison, and Social Learning. The results from the data analysis reveal that, in general, participants perceived all of the four strat- egies as persuasive, as they were rated above the neutral value of 3.5. This implies that each of the strategies has the potential of motivating behavioral change when imple- mented in persuasive applications. Overall, regardless of gender and age, Reward, fol- lowed by Competition, emerged as the most persuasive strategies, while Social Com- parison and Social Learning emerged as the least persuasive strategies. Moreover, with respect to gender, we found that males are more motivated by Reward and Competition than females. Similarly, with respect to age, we found that younger people are more motivated by Competition, Social Learning, and Social Comparison. Specifically, younger males are more motivated by Competition, while older females are least moti- vated by Competition. This indicates that while Competition may be a very effective strategy for younger males, it is less effective for older females, thereby highlighting the need to tailor persuasive strategies to increase their effectiveness. In conclusion, in order to increase the effectiveness of persuasive technology, our findings can guide designers in deciding the best strategies to employ when designing persuasive applica- tions for various user groups based on age and gender.
References
1. Chatterjee, S., Price, A.: Healthy Living with Persuasive Technologies: Framework, Issues, and Challenges. Journal of the American Medical Informatics Association. 16, 171–178 (2009).
2. Yoganathan, D., Kajanan, S.: Persuasive Technology for Smartphone Fitness. In: PACIS 2013 Proceedings. pp. 1–11 (2013).
3. Orji, R., Mandryk, R.L., Vassileva, J., Gerling, K.M.: Tailoring persuasive health games to gamer type. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. pp. 2467–2476. ACM (2013).
4. Orji, R.O.: Design for Behaviour Change: A Model-driven Approach for Tailoring Persuasive Technologies. University of Saskatchewan, Canada. 1–257 (2014).
5. Stibe, A., Oinas-Kukkonen, H.: Comparative analysis of recognition and competition as features of social influence using Twitter. In: International Conference on Persuasive Technology. pp. 274–279. Springer Berlin Heidelberg (2012).
6. Cialdini, R., Trost, M.: Social influence: Social norms, conformity and compliance. In: The Handbook of Social Psychology, Vol. 2. pp. 151–192 (1998).
7. Oyibo, K.: Designing Culture-based Persuasive Technology to Promote Physical Activity among University Students. In: 24th Conference on User Modeling, Adaptation and Personalization (UMAP 2016). pp. 321–324. , Halifax, Canada (2016).
8. Busch, M., Schrammel, J., Tscheligi, M.: Personalized persuasive technology - Development and validation of scales for measuring persuadability. In: International Conference on Persuasive Technology. pp. 33–38. Springer Berlin Heidelberg (2013).
9. Incentive Theory of Motivation and Intrinsic vs. Extrinsic Motivation, https://www.boundless.com/psychology/textbooks/boundless-psychology-
textbook/motivation-12/theories-of-motivation-65/incentive-theory-of-motivation-and- intrinsic-vs-extrinsic-motivation-252-12787/.
10. Cherry, K.: The Incentive Theory of Motivation,
http://psychology.about.com/od/motivation/a/incentive-theory-of-motivation.htm.
11. Oyibo, K., Vassileva, J.: Investigation of Social Predictors of Competitive Behavior in Persuasive Technology. In: The 12th International Conference on Persuasive Technology (PT’17). Springer, Amsterdam, Netherlands (2017).
12. Oinas-kukkonen, H., Harjumaa, M.: Persuasive Systems Design : Key Issues , Process Model, and System Features. Communications of the Association for Information Systems.
24, 485–500 (2009).
13. Mead, M.: Cooperation and competition among primitive people. Transaction Publishers.
123, (2002).
14. Deutsch, M.: Theory of Cooperation and Competition. Sage Socia. 129–152.
15. Orji, R., Vassileva, J., Mandryk, R.L.: Modeling the efficacy of persuasive strategies for different gamer types in serious games for health. User Modeling and User-Adapted Interaction. 24, 453–498 (2014).
16. Stibe, A.: Socially Influencing Systems: Persuading People to Engage with Publicly Displayed Twitter-Based Systems, (2014).
17. Festinger, L.: A theory of social comparison. Human Relations. 7, 117–140 (1954).
18. Garcia, S.M., Tor, A., Schiff, T.M.: The Psychology of Competition: A Social Comparison Perspective. Perspectives on Psychological Science. 8, 634–650 (2013).
19. Orji, R., Nacke, L.E., DiMarco, C.: Towards Personality-driven Persuasive Health Games and Gamified Systems. Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. (2017).
20. Richter, G., Raban, D.R., Rafaeli, S.: Studying gamification: The effect of rewards and incentives on motivation. In: Gamification in Education and Business. pp. 21–46 (2015).
21. Bandura, A.: Social learning theory. Stanford University. 1–46 (1971).
22. Kaptein, M.: Adaptive Persuasive Messages In An E-Commerce Setting : The Use Of Persuasion Profiles. Ecis. Paper 183 (2011).
23. Kaptein, M., De Ruyter, B., Markopoulos, P., Aarts, E.: Adaptive Persuasive Systems: A Study of Tailored Persuasive Text Messages to Reduce Snacking. ACM Transactions on Interactive Intelligent Systems. 2, 1–25 (2012).
24. Niederle, M., Vesterlund, L.: Gender and Competition. Annual Review of Economics. 3, 601–630 (2011).
25. Li, C. shan R., Huang, C.Y., Lin, W. yu, Sun, C.W.V.: Gender differences in punishment and reward sensitivity in a sample of Taiwanese college students. Personality and Individual Differences. 43, 475–483 (2007).
26. Nicksic, T., Ducharme, A.: Gender Differences in the Relationship of Downward Social Comparison to Self-esteem. 1–4 (2004).
27. Franzoi, S.L., Vasquez, K., Sparapani, E., Frost, K., Martin, J., Aebly, M.: Exploring Body Comparison Tendencies: Women Are Self-Critical Whereas Men Are Self-Hopeful.
Psychology of Women Quarterly. 36, 99–109 (2012).
28. Orji, R., Mandryk, R.L., Vassileva, J.: Gender, age, and responsiveness to Cialdini’s persuasion strategies. In: International Conference on Persuasive Technology. pp. 147–159.
Springer International Publishing (2015).
29. Yee, N.: As Gamers Age, The Appeal of Competition Drops The Most. Strategy is The Most
Age-Stable Motivation, http://quanticfoundry.com/2016/02/10/gamer-generation/.
30. Sproten, A.N., Schwieren, C.: Age differences in the reaction to incentives – do older people avoid competition? No. 522. Discussion Paper Series, University of Heidelberg, Department of Economics. (2012).
31. Steinberg, L., Monahan, K.C.: Age Differences in Resistance to Peer Influence. Dev Psychol. 43, 1531–1543 (2009).
32. Dunn, T.J., Baguley, T., Brunsden, V.: From alpha to omega: A practical solution to the pervasive problem of internal consistency estimation. British Journal of Psychology. 105, 399–412 (2014).
33. Ghadi, I., Hayati Alwi, N., Abu Bakar, K., Talib, O.: Construct Validity Examination of Critical Thinking Dispositions for Undergraduate Students in University Putra Malaysia.
Higher Education Studies. 2, (2012).
34. Hamari, J., Koivisto, J., Pakkanen, T.: Do Persuasive Technologies Persuade?-A Review of Empirical Studies. Persuasive Technology. 8462, 118–136 (2014).
35. Orji, R.: Gender and Persuasive Technology: Examining the Persuasiveness of Persuasive Strategies by Gender Groups. In: The 9th International Conference on Persuasive Technology (PERSUASIVE 2014) (2014).
36. Busch, M., Mattheiss, E., Reisinger, M., Orji, R., Fröhlich, P., Tscheligi, M.: More than sex: The role of femininity and masculinity in the design of personalized persuasive games.
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 9638, 219–229 (2016).
